{"id":"W2950120176","doi":"10.48550/arxiv.1705.08844","title":"How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Commonsense knowledge; Computer science; Exploit; Ontology; Commonsense reasoning; Artificial intelligence; Information retrieval; Natural language processing; Question answering; Testbed; Benchmark (surveying); Sentence; Knowledge retrieval; Knowledge representation and reasoning; Knowledge extraction; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003190074,0.001391131,0.001088533,0.002535635,0.001050944,0.001891315,0.001825638,0.001894425,0.004088747],"category_scores_gemma":[0.009339235,0.0003184331,0.00117912,0.002192545,0.0008817603,0.007766274,0.002180289,0.001765651,0.002200162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001699358,"about_ca_system_score_gemma":0.001820219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01604599,"about_ca_topic_score_gemma":0.01976569,"domain_scores_codex":[0.9981437,0.00040452,0.0001950955,0.0004942174,0.0005552987,0.0002071808],"domain_scores_gemma":[0.9977089,0.0008082555,0.0001061304,0.0008503735,0.0004312544,0.00009506648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006019224,0.0006616109,0.005643236,0.0007407088,0.0002587462,0.0002104236,0.0002979901,0.03951507,0.03140802,0.01070653,0.03545066,0.8745051],"study_design_scores_gemma":[0.0001620587,0.0005561428,0.004634811,0.0001346947,0.0003344613,0.000596834,0.0007425315,0.8373792,0.063587,0.04759942,0.04415653,0.0001162614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3676319,0.008136529,0.5497443,0.003948923,0.0008855616,0.0007810442,0.004558057,0.03491627,0.02939739],"genre_scores_gemma":[0.676183,0.001365922,0.3063006,0.0009289739,0.000127547,0.0001343046,0.01026181,0.0005069922,0.004190832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01604599,"threshold_uncertainty_score":0.03190517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04332595075993373,"score_gpt":0.2097428559751148,"score_spread":0.1664169052151811,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}